Investigating Spatial Dynamics in Spatial Omics Data with StarTrail
这篇论文提出基于梯度的StarTrail方法，用于在空间组学数据中识别快速变化的区域和表达剧变的“悬崖基因”，并量化方向动态，能更清晰勾勒大脑分层和肿瘤免疫边界等生物学结构。
Spatial omics technologies revolutionize our view of biological processes within tissues. However, existing methods fail to capture localized, sharp changes characteristic of critical events (e.g., tumor development). Here, we present StarTrail, a novel gradient based method that powerfully defines rapidly changing regions and detects “cliff genes”, genes exhibiting drastic expression changes at highly localized or disjoint boundaries. StarTrail, the first to leverage spatial gradients for spatial omics data, also quantifies directional dynamics. Across multiple datasets, StarTrail accurately delineates boundaries (e.g., brain layers, tumor-immune boundaries), and detects cliff genes that may regulate molecular crosstalk at these biologically relevant boundaries but are missed by existing methods. StarTrail, filling important gaps in current literature, enables deeper insights into tissue spatial architecture. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.